Abstract: A system is provided for localizing parts of an object in an image by training local detectors using labeled image exemplars with fiducial points corresponding to parts within the image. Each local detector generates a detector score corresponding to the likelihood that a desired part is located at a given location within the image exemplar. A non-parametric global model of the locations of the fiducial points is generated for each of at least a portion of the image exemplars. An input image is analyzed using the trained local detectors, and a Bayesian objective function is derived for the input image from the non-parametric model and detector scores. The Bayesian objective function is optimized using a consensus of global models, and an output is generated with locations of the fiducial points labeled within the object in the image.

Abstract: A system is provided for localizing parts of an object in an image by training local detectors using labeled image exemplars with fiducial points corresponding to parts within the image. Each local detector generates a detector score corresponding to the likelihood that a desired part is located at a given location within the image exemplar. A non-parametric global model of the locations of the fiducial points is generated for each of at least a portion of the image exemplars. An input image is analyzed using the trained local detectors, and a Bayesian objective function is derived for the input image from the non-parametric model and detector scores. The Bayesian objective function is optimized using a consensus of global models, and an output is generated with locations of the fiducial points labeled within the object in the image.

Abstract: A method is provided for localizing parts of an object in an image by training local detectors using labeled image exemplars with fiducial points corresponding to parts within the image. Each local detector generates a detector score corresponding to the likelihood that a desired part is located at a given location within the image exemplar. A non-parametric global model of the locations of the fiducial points is generated for each of at least a portion of the image exemplars. An input image is analyzed using the trained local detectors, and a Bayesian objective function is derived for the input image from the non-parametric model and detector scores. The Bayesian objective function is optimized using a consensus of global models, and an output is generated with locations of the fiducial points labeled within the object in the image.

Abstract: A method is provided for localizing parts of an object in an image by training local detectors using labeled image exemplars with fiducial points corresponding to parts within the image. Each local detector generates a detector score corresponding to the likelihood that a desired part is located at a given location within the image exemplar. A non-parametric global model of the locations of the fiducial points is generated for each of at least a portion of the image exemplars. An input image is analyzed using the trained local detectors, and a Bayesian objective function is derived for the input image from the non-parametric model and detector scores. The Bayesian objective function is optimized using a consensus of global models, and an output is generated with locations of the fiducial points labeled within the object in the image.

Abstract: A stormwater filtration system includes a stormwater containment structure including a bottom surface, an inlet that receives stormwater and an outlet through which filtered stormwater exits the stormwater containment structure. A flow structure is at the bottom surface. The flow structure includes a conveyance conduit running along the bottom surface of the stormwater containment structure and in fluid communication with the outlet of the stormwater containment structure. The conveyance conduit includes a port extending through a sidewall of the conveyance conduit. A saddle includes an upper component and a lower component connected to the upper component such that the upper and lower components straddle the conveyance conduit at the port. The upper component includes an opening in communication with the port and the lower component supports the conveyance conduit above the bottom surface.

Abstract: A stormwater filtration system includes a stormwater containment structure including a bottom surface, an inlet that receives stormwater and an outlet through which filtered stormwater exits the stormwater containment structure. A flow structure is at the bottom surface. The flow structure includes a conveyance conduit running along the bottom surface of the stormwater containment structure and in fluid communication with the outlet of the stormwater containment structure. The conveyance conduit includes a port extending through a sidewall of the conveyance conduit. A saddle includes an upper component and a lower component connected to the upper component such that the upper and lower components straddle the conveyance conduit at the port. The upper component includes an opening in communication with the port and the lower component supports the conveyance conduit above the bottom surface.

Abstract: A stormwater filtration system includes a stormwater containment structure including a bottom surface, an inlet that receives stormwater and an outlet through which filtered stormwater exits the stormwater containment structure. A flow structure is at the bottom surface. The flow structure includes a conveyance conduit running along the bottom surface of the stormwater containment structure and in fluid communication with the outlet of the stormwater containment structure. The conveyance conduit includes a port extending through a sidewall of the conveyance conduit. A saddle includes an upper component and a lower component connected to the upper component such that the upper and lower components straddle the conveyance conduit at the port. The upper component includes an opening in communication with the port and the lower component supports the conveyance conduit above the bottom surface.

Abstract: A stormwater filtration system includes a stormwater containment structure including a bottom surface, an inlet that receives stormwater and an outlet through which filtered stormwater exits the stormwater containment structure. A flow structure is at the bottom surface. The flow structure includes a conveyance conduit running along the bottom surface of the stormwater containment structure and in fluid communication with the outlet of the stormwater containment structure. The conveyance conduit includes a port extending through a sidewall of the conveyance conduit. A saddle includes an upper component and a lower component connected to the upper component such that the upper and lower components straddle the conveyance conduit at the port. The upper component includes an opening in communication with the port and the lower component supports the conveyance conduit above the bottom surface.

Abstract: The present invention is a method of deriving a reflectance function that analytically approximates the light reflected from an object model in terms of the spherical harmonic components of light. The reflectance function depends upon the intensity of light incident at each point on the model, the intensity of light diffusely reflected, and the intensity of light broadened-specularly reflected in the direction of an observer. This reflectance function is used in the process of machine vision, by allowing a machine to optimize the reflectance function and arrive at an optimal rendered image of the object model, relative to an input image. Therefore, the recognition of an image produced under variable lighting conditions is more robust. The reflectance function of the present invention also has applicability in other fields, such as computer graphics.

Abstract: The present invention is a method of deriving a reflectance function that analytically approximates the light reflected from an object model in terms of the spherical harmonic components of light. The reflectance function depends upon the intensity of light incident at each point on the model, but excludes light originating from below a local horizon, therefore not contributing to the reflectance because of the cast shadows. This reflectance function is used in the process of machine vision, by allowing a machine to optimize the reflectance function and arrive at an optimal rendered image of the object model, relative to an input image. Therefore, the recognition of an image produced under variable lighting conditions is more robust. The reflectance function of the present invention also has applicability in other fields, such as computer graphics.

Abstract: The Torrance-Sparrow model of off-specular reflection is recast in a significantly simpler and more transparent form in order to render a spherical-harmonic decomposition more feasible. By assuming that a physical surface consists of small, reflecting facets whose surface normals satisfy a normal distribution, the model captures the off-specular enhancement of the reflected intensity distribution often observed at large angles of incidence and reflection, features beyond the reach of the phenomenological broadening models usually employed. In passing we remove a physical inconsistency in the original treatment, restoring reciprocity and correcting the dependence of reflectance on angle near grazing incidence. It is noted that the results predicted by the model are relatively insensitive to values of its one parameter, the width of the distribution of surface normals.

Abstract: A method for choosing an image from a plurality of three-dimensional models which is most similar to an input image is provided.

Abstract: The Torrance-Sparrow model of off-specular reflection is recast in a significantly simpler and more transparent form in order to render a spherical-harmonic decomposition more feasible. By assuming that a physical surface consists of small, reflecting facets whose surface normals satisfy a normal distribution, the model captures the off-specular enhancement of the reflected intensity distribution often observed at large angles of incidence and reflection, features beyond the reach of the phenomenological broadening models usually employed. In passing we remove a physical inconsistency in the original treatment, restoring reciprocity and correcting the dependence of reflectance on angle near grazing incidence. It is noted that the results predicted by the model are relatively insensitive to values of its one parameter, the width of the distribution of surface normals.

Abstract: The present invention is a method of deriving a reflectance function that analytically approximates the light reflected from an object model in terms of the spherical harmonic components of light. The reflectance function depends upon the intensity of light incident at each point on the model, but excludes light originating from below a local horizon, therefore not contributing to the reflectance because of the cast shadows. This reflectance function is used in the process of machine vision, by allowing a machine to optimize the reflectance function and arrive at an optimal rendered image of the object model, relative to an input image. Therefore, the recognition of an image produced under variable lighting conditions is more robust. The reflectance function of the present invention also has applicability in other fields, such as computer graphics.

Abstract: The present invention is a method of deriving a reflectance function that analytically approximates the light reflected from an object model in terms of the spherical harmonic components of light. The reflectance function depends upon the intensity of light incident at each point on the model, the intensity of light diffusely reflected, and the intensity of light broadened-specularly reflected in the direction of an observer. This reflectance function is used in the process of machine vision, by allowing a machine to optimize the reflectance function and arrive at an optimal rendered image of the object model, relative to an input image. Therefore, the recognition of an image produced under variable lighting conditions is more robust. The reflectance function of the present invention also has applicability in other fields, such as computer graphics.

Abstract: A method of marking glass includes providing a glass material with a surface (201) and using a wire (210) to mark the surface. The wire is softer than the glass material and does not damage the surface of the glass material.

Abstract: A field emission device (100, 150) includes a cathode plate (102, 180) having electron emitters (116), an anode plate (104, 170) having a phosphor (107, 207, 307, 407) activated by electrons (119) emitted by electron emitters (116), and a vacuum bridge focusing structure (118, 158, 218, 318) for focusing electrons (119) emitted by electron emitters (116). Vacuum bridge focusing structure (118, 158, 218, 318) has landings (121, 122, 221, 322), which are attached to cathode plate (102, 180), and further has bridges (120, 220, 320), which extend above and beyond landings (121, 122, 221, 322, 421) to provide a self-supporting structure that is spaced apart from cathode plate (102, 180).

Abstract: A method for generating a complete scene structure from a video sequence that provides incomplete data. The method has a first step of building a first matrix consisting of point locations from a motion sequence by acquiring a sequence of images of a fixed scene using a moving camera; identifying and tracking point features through the sequence; and using the coordinates of the features to build the first matrix with some missing elements where some features are not present in some images. In a second step an approximate solution is built by selecting triples of columns from the first matrix; forming their nullspaces into a second matrix; and taking the three smallest components of the second matrix. In a third step, an iterative algorithm is applied to the three smallest components to build a third matrix and to improve the estimate. Lastly, in a fourth step the third matrix is decomposed to determine the complete scene structure.